Remote ML Engineer – AI Training & Pipelines (Part-Time)

Synthires; United States; Remote; USD 83,000 - 165,000; Part time

Job summary

Synthires is seeking a qualified ML Engineer (MSc or PhD) for a remote, contractor role. The position is part-time (approximately 15 hours/week) with compensation of $60–$120/hour. Worldwide remote location.

This role emphasizes Python coding, reproducible workflows, and engineering tasks in model development, training, and inference systems. Ideal candidates will have advanced degrees in quantitative fields, strong ML experience, and a proven ability to implement and debug complex ML workflows.

Qualifications

• Master's degree or PhD in Computer Science, Machine Learning, AI, Applied Mathematics, Statistics, Engineering, or a related quantitative field.
• Strong professional or research experience in machine learning.
• Practical proficiency in Python and experience using coding agents.
• Ability to implement, debug, and validate complex machine learning workflows.

Responsibilities

• Develop and validate machine learning models, training pipelines, and inference systems.
• Implement model components, data pipelines, evaluation systems, and numerical methods.
• Build reproducible workflows using Python and command-line tools.
• Debug numerical and system-level issues and optimize memory usage and performance.
• Create, solve, review, and validate challenging machine learning engineering tasks.

Skills

Python

ML proficiency

Research experience

Coding pipelines

Education

Master's or PhD in CS/ML/Applied Math/Engineering

Tools

Coding agents

Job description

Synthires is seeking a qualified ML Engineer (MSc or PhD) for a remote, contractor role. The position is part-time (approximately 15 hours/week) with compensation of $60–$120/hour. Worldwide remote location.

This role emphasizes Python coding, reproducible workflows, and engineering tasks in model development, training, and inference systems. Ideal candidates will have advanced degrees in quantitative fields, strong ML experience, and a proven ability to implement and debug complex ML workflows.